【项目三、车牌检测+识别项目】四、使用LPRNet进行车牌识别
目录一、CCPD数据集介绍一、CCPD数据集介绍CCPD车牌数据集是采集人员在合肥停车场采集、手工标注得来,采集时间在早7:30到晚10:00之间。且拍摄车牌照片的环境复杂多变,包括雨天、雪天、倾斜、模糊等。CCPD数据集包含将近30万张图片、图片尺寸为720x1160x3,共包含8种类型图片,每种类型、数量及类型说明如下表:类型图片数说明ccpd_base199998正常车牌ccpd_chall
前言
马上要找工作了,想总结下自己做过的几个小项目。
之前已经总结过了我做的第一个项目:xxx病虫害检测项目,github源码地址:HuKai97/FFSSD-ResNet。CSDN讲解地址:
- 【项目一、xxx病虫害检测项目】1、SSD原理和源码分析
- 【项目一、xxx病虫害检测项目】2、网络结构尝试改进:Resnet50、SE、CBAM、Feature Fusion
- 【项目一、xxx病虫害检测项目】3、损失函数尝试:Focal loss
第二个项目:蜂巢检测项目,github源码地址:https://github.com/HuKai97/YOLOv5-ShuffleNetv2。CSDN讲解地址:
【项目二、蜂巢检测项目】一、串讲各类经典的卷积网络:InceptionV1-V4、ResNetV1-V2、MobileNetV1-V3、ShuffleNetV1-V2、ResNeXt、Xception。
【项目二、蜂巢检测项目】二、模型改进:YOLOv5s-ShuffleNetV2。
如果对YOLOv5不熟悉的同学可以先看看我写的YOLOv5源码讲解CSDN:【YOLOV5-5.x 源码讲解】整体项目文件导航,注释版YOLOv5源码我也开源在了Github上:HuKai97/yolov5-5.x-annotations,欢迎大家star!
之前一直在学习OCR相关的东西,就想着能不能做一个车牌识别的项目出来,刚好车牌检测也好做,直接用v5就可以了。我的打算是做一个轻量级的车牌识别项目,检测网络用的是YOLOv5s,识别网络有的是LPRNet。
这一节主要介绍下怎么训练LPRNet车牌识别模型。
车牌识别项目所有讲解:
- 【项目三、车牌检测+识别项目】一、CCPD车牌数据集转为YOLOv5格式和LPRNet格式
- 【项目三、车牌检测+识别项目】二、使用YOLOV5进行车牌检测
- 【项目三、车牌检测+识别项目】三、LPRNet车牌识别网络原理和核心源码解读
- 【项目三、车牌检测+识别项目】四、使用LPRNet进行车牌识别
代码已全部上传GitHub:https://github.com/HuKai97/YOLOv5-LPRNet-Licence-Recognition
一、数据集
在项目文件 YOLOv5-LRPNet-Licence-Recognition 的同级目录建一个datasets文件,数据集这里写的很清楚:【项目三、车牌检测+识别项目】一、CCPD车牌数据集转为YOLOv5格式和LPRNet格式,制作好放在在datasets里面,我一般不会把数据和代码放在一起,那样pycharm打开非常慢。
二、训练
在tools下的train_lprnet.py,主要配置:
def get_parser():
parser = argparse.ArgumentParser(description='parameters to train net')
parser.add_argument('--max_epoch', default=100, help='epoch to train the network')
parser.add_argument('--img_size', default=[94, 24], help='the image size')
parser.add_argument('--train_img_dirs', default=r"", help='the train images path')
parser.add_argument('--test_img_dirs', default=r"", help='the test images path')
parser.add_argument('--dropout_rate', default=0.5, help='dropout rate.')
parser.add_argument('--learning_rate', default=0.01, help='base value of learning rate.')
parser.add_argument('--lpr_max_len', default=8, help='license plate number max length.')
parser.add_argument('--train_batch_size', default=128, help='training batch size.')
parser.add_argument('--test_batch_size', default=128, help='testing batch size.')
parser.add_argument('--phase_train', default=True, type=bool, help='train or test phase flag.')
parser.add_argument('--num_workers', default=8, type=int, help='Number of workers used in dataloading')
parser.add_argument('--cuda', default=True, type=bool, help='Use cuda to train model')
parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining')
parser.add_argument('--save_interval', default=500, type=int, help='interval for save model state dict')
parser.add_argument('--test_interval', default=500, type=int, help='interval for evaluate')
parser.add_argument('--momentum', default=0.9, type=float, help='momentum')
parser.add_argument('--weight_decay', default=2e-5, type=float, help='Weight decay for SGD')
parser.add_argument('--lr_schedule', default=[20, 40, 60, 80, 100], help='schedule for learning rate.')
parser.add_argument('--save_folder', default=r'',
help='Location to save checkpoint models')
parser.add_argument('--pretrained_model', default='', help='no pretrain')
另外,我是没加载预训练权重的,一开始训不好,后来发现调好learning_rate和lr_schedule这两个参数,一般都能训到不错的分数(93+)。
三、验证
在tools下的test_lprnet.py,主要配置:
def get_parser():
parser = argparse.ArgumentParser(description='parameters to train net')
parser.add_argument('--img_size', default=[94, 24], help='the image size')
parser.add_argument('--test_img_dirs', default=r"", help='the test images path')
parser.add_argument('--dropout_rate', default=0, help='dropout rate.')
parser.add_argument('--lpr_max_len', default=8, help='license plate number max length.')
parser.add_argument('--test_batch_size', default=100, help='testing batch size.')
parser.add_argument('--phase_train', default=False, type=bool, help='train or test phase flag.')
parser.add_argument('--num_workers', default=0, type=int, help='Number of workers used in dataloading')
parser.add_argument('--cuda', default=True, type=bool, help='Use cuda to train model')
parser.add_argument('--show', default=False, type=bool, help='show test image and its predict result or not.')
parser.add_argument('--pretrained_model', default=r'', help='pretrained base model')
LPRNet算法性能:
model | 数据集 | epochs | acc | size |
---|---|---|---|---|
LPRNet | val | 100 | 94.33 | 1.7M |
LPRNet | test | 100 | 94.30 | 1.7M |
整个模型(YOLOv5+LPRNet)速度:47.6FPS(970 GPU)
四、测试结果
当然也有检测的不好的:
五、推理代码
main.py
import argparse
import torch.backends.cudnn as cudnn
from models.experimental import *
from utils.datasets import *
from utils.utils import *
from models.LPRNet import *
def detect(save_img=False):
classify, out, source, det_weights, rec_weights, view_img, save_txt, imgsz = \
opt.classify, opt.output, opt.source, opt.det_weights, opt.rec_weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
device = torch_utils.select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete rec_result folder
os.makedirs(out) # make new rec_result folder
half = device.type != 'cpu' # half precision only supported on CUDA
# Load yolov5 model
model = attempt_load(det_weights, map_location=device) # load FP32 model
print("load det pretrained model successful!")
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier 也就是rec 字符识别
if classify:
modelc = LPRNet(lpr_max_len=8, phase=False, class_num=len(CHARS), dropout_rate=0).to(device)
modelc.load_state_dict(torch.load(rec_weights, map_location=torch.device('cpu')))
print("load rec pretrained model successful!")
modelc.to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size demo
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
# Run demo
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = torch_utils.time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = torch_utils.time_synchronized()
# Apply Classifier
if classify:
pred, plat_num = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, 5].unique():
n = (det[:, 5] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for de, lic_plat in zip(det, plat_num):
# xyxy,conf,cls,lic_plat=de[:4],de[4],de[5],de[6:]
*xyxy, conf, cls=de
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * 5 + '\n') % (cls, xywh)) # label format
if save_img or view_img: # Add bbox to image
# label = '%s %.2f' % (names[int(cls)], conf)
lb = ""
for a,i in enumerate(lic_plat):
# if a ==0:
# continue
lb += CHARS[int(i)]
label = '%s %.2f' % (lb, conf)
im0=plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (demo + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # rec_result video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % os.getcwd() + os.sep + out)
if platform == 'darwin': # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--classify', nargs='+', type=str, default=True, help='True rec')
parser.add_argument('--det-weights', nargs='+', type=str, default='./weights/yolov5_best.pt', help='model.pt path(s)')
parser.add_argument('--rec-weights', nargs='+', type=str, default='./weights/lprnet_best.pth', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='./demo/images/', help='source') # file/folder, 0 for webcam
parser.add_argument('--rec_result', type=str, default='demo/rec_result', help='rec_result folder') # rec_result folder
parser.add_argument('--img-size', type=int, default=640, help='demo size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented demo')
parser.add_argument('--update', action='store_true', help='update all models')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
if opt.update: # update all models (to fix SourceChangeWarning)
for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov3-spp.pt']:
detect()
create_pretrained(opt.weights, opt.weights)
else:
detect()
Reference
CSDN: linux-mobaxterm-yolov5训练数据集ccpd–无数踩雷后
Github: https://github.com/ultralytics/yolov5
Github: https://github.com/sirius-ai/LPRNet_Pytorch
Gitee: https://gitee.com/reason1251326862/plate_classification
Github:https://github.com/kiloGrand/License-Plate-Recognition
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